Prevalence of self-medication during COVID-19 pandemic: A systematic review and meta-analysis
Bibliographic record
Abstract
Background The COVID-19 pandemic and restrictions on travel and quarantine measures made people turn to self-medication (SM) to control the symptoms of their diseases. Different studies were conducted worldwide on different populations, and their results were different. Therefore, this global systematic review and meta-analysis was conducted to estimate the pooled prevalence of self-medication. Methods In this systematic review and meta-analysis, databases of Scopus, PubMed, Embase, and Web of Science were searched without a time limit. All eligible observational articles that reported self-medication during the COVID-19 pandemic were analyzed. Heterogeneity among the studies was assessed using Cochran's Q test and I 2 statistics. A random-effects model was used to estimate the pooled prevalence of self-medication. The methodological quality of the articles was evaluated with the Newcastle-Ottawa Scale. Results Fifty-six eligible studies were reviewed. The pooled prevalence of self-medication was 48.6% (95% CI: 42.8–54.3). The highest and lowest prevalence of self-medication was in Asia (53%; 95% CI: 45–61) and Europe (40.8%; 95% CI: 35–46.8). Also, the highest and lowest prevalence of self-medication was related to students (54.5; 95% CI: 40.8–68.3) and healthcare workers (32.5%; 16–49). The prevalence of self-medication in the general population (48.8%; 40.6–57) and in patients with COVID-19 (41.7%; 25.5–58). The prevalence of self-medication was higher in studies that collected data in 2021 than in 2020 (51.2 vs. 48%). Publication bias was not significant ( p = 0.320). Conclusion During the COVID-19 pandemic, self-medication was highly prevalent, so nearly half of the people self-medicated. Therefore, it seems necessary to provide public education to control the consequences of self-medication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".